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Record W1891575594 · doi:10.1139/cgj-2015-0199

New approach to improve soil-water characteristic curve to reduce variation in estimation of unsaturated permeability function

2015· article· en· W1891575594 on OpenAlexvenueno aff
Arezoo Rahimi, Harianto Rahardjo

Bibliographic record

VenueCanadian Geotechnical Journal · 2015
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsnot available
Fundersnot available
KeywordsPermeameterPermeability (electromagnetism)SuctionGeotechnical engineeringSoil waterEnvironmental scienceMathematicsSoil scienceMaterials scienceGeologyHydraulic conductivityEngineeringChemistry

Abstract

fetched live from OpenAlex

The unsaturated permeability function is often estimated from the soil-water characteristic curve (SWCC) of a soil. A complete SWCC measurement can improve the estimation of the unsaturated permeability function. In most laboratories, the SWCC can be measured up to a suction of 100 kPa using a Tempe cell. However, complete measurement of the SWCC is an expensive and time-consuming task. Therefore, this paper presents a new approach to estimate SWCC data points beyond 100 kPa suction to complement the SWCC measured up to a suction of 100 kPa. The new SWCC is then used to estimate the unsaturated permeability function. The proposed approach uses knowledge of the grain-size distribution curve and measured SWCC data at 100 kPa suction to estimate the SWCC data points beyond 100 kPa suction. To verify the proposed procedure, SWCC tests were conducted over a wide range of suctions for coarse kaolin and a triaxial permeameter system was used to directly measure unsaturated permeability of the coarse kaolin. The proposed procedure is found to reduce the variation between unsaturated permeability functions estimated by various estimation models.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.215
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations22
Published2015
Admission routes1
Has abstractyes

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